Scatter Plot

Version 2025-10-11

Now works on Igor 9.

Tutorial: https://www.bilibili.com/video/BV1oEWtz7ECS/

https://youtu.be/sHM2i9ND3VU

Scatter Plot is a useful toolkit based on Igor Pro, designed to enhance data visualization efficiency in atmospheric science. While many existing generalized data visualization software options have been widely used, they often fall short in meeting specific research needs in this field. This gap motivated the development of Scatter Plot. The program incorporates the Deming and York algorithms for linear regression, which account for uncertainties in both the X and Y variables, making it more suitable for atmospheric applications. Scatter Plot is equipped with various features for data analysis and graph plotting, including batch plotting, data masking through a user-friendly interface, color coding along the Z-axis, and the ability to filter and group data across different time scales—such as year, season, month, hour, and day of the week.

For more details regarding the evaluation and application of Scatter Plot, please refer to 

For more details regarding the evaluation and application of Scatter Plot, please refer to 

Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi: https://doi.org/10.5194/amt-11-1233-2018, 2018.

Please cite this paper if Scatter Plot is used in your publication.

The latest version of the program can be found on my website:

https://wucheng.weebly.com/

https://sites.google.com/site/wuchengust/

https://doi.org/10.5281/zenodo.832416

Scatter Plot toolkit adoption in research publications:

Wu, Y., Liu, D., Xu, H., Shan, M., Li, S., Tian, P., Hu, K., and Wang, J.: Analysis of atmospheric pollutant characteristics and regional transport in coastal area along the East China Sea, J. Environ. Sci., 156, 225-238, doi: https://doi.org/10.1016/j.jes.2024.06.040, 2025.

Li, W., Qi, Y., Liu, Y., Wu, G., Zhang, Y., Shi, J., Qu, W., Sheng, L., Wang, W., Zhang, D., and Zhou, Y.: Daytime and nighttime aerosol soluble iron formation in clean and slightly polluted moist air in a coastal city in eastern China, Atmos. Chem. Phys., 24, 6495-6508, doi: https://doi.org/10.5194/acp-24-6495-2024, 2024.

Wu, B., Wu, C., Ye, Y., Pei, C., Deng, T., Li, Y. J., Lu, X., Wang, L., Hu, B., Li, M., and Wu, D.: Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China, Atmos. Environ., 321, 120347, doi: https://doi.org/10.1016/j.atmosenv.2024.120347, 2024.

Hedges, M., Priestman, M., Chadeau-Hyam, M., Sinharay, R., Kelly, F. J., and Green, D. C.: Characterising a mobile reference station (MoRS) to quantify personal exposure to air quality, Atmos. Environ., 315, 120160, doi: https://doi.org/10.1016/j.atmosenv.2023.120160, 2023.

Lin, W., Lai, Y., Zhuang, S., Wei, Q., Zhang, H., Hu, Q., Cheng, P., Zhang, M., Zhai, Y., Wang, Q., Han, Z., and Hou, H.: The effects of prenatal PM2.5 oxidative potential exposure on feto-placental vascular resistance and fetal weight: A repeated-measures study, Environ. Res., 234, 116543, doi: https://doi.org/10.1016/j.envres.2023.116543, 2023.

Liu, H., Jia, M., Tao, J., Yao, D., Li, J., Zhang, R., Li, L., Xu, M., Fan, Y., Liu, Y., and Cheng, K.: Elucidating pollution characteristics, temporal variation and source origins of carbonaceous species in Xinxiang, a heavily polluted city in North China, Atmos. Environ., 298, 119626, doi: https://doi.org/10.1016/j.atmosenv.2023.119626, 2023.

Liu, Y., Zhou, M., Zhao, M., Jing, S., Wang, H., Lu, K., and Shen, H.: Determination of Urban Formaldehyde Emission Ratios in the Shanghai Megacity, Environ. Sci. Technol., 57, 16489-16499, doi: https://doi.org/10.1021/acs.est.3c06428, 2023.

Peng, Y., Wang, H., Wang, Q., Jing, S., An, J., Gao, Y., Huang, C., Yan, R., Dai, H., Cheng, T., Zhang, Q., Li, M., Hu, J., Shi, Z., Li, L., Lou, S., Tao, S., Hu, Q., Lu, J., and Chen, C.: Observation-based sources evolution of non-methane hydrocarbons (NMHCs) in a megacity of China, J. Environ. Sci., 124, 794-805, doi: https://doi.org/10.1016/j.jes.2022.01.040, 2023.

Song, Z., Gao, W., Shen, H., Jin, Y., Zhang, C., Luo, H., Pan, L., Yao, B., Zhang, Y., Huo, J., Sun, Y., Yu, D., Chen, H., Chen, J., Duan, Y., Zhao, D., and Xu, J.: Roles of Regional Transport and Vertical Mixing in Aerosol Pollution in Shanghai Over the COVID-19 Lockdown Period Observed Above Urban Canopy, J. Geophys. Res., 128, e2023JD038540, doi: https://doi.org/10.1029/2023JD038540, 2023.

Young, L.-H., Hsu, C.-S., Hsiao, T.-C., Lin, N.-H., Tsay, S.-C., Lin, T.-H., Lin, W.-Y., and Jung, C.-R.: Sources, transport, and visibility impact of ambient submicrometer particle number size distributions in an urban area of central Taiwan, Sci. Total. Environ., 856, 159070, doi: https://doi.org/10.1016/j.scitotenv.2022.159070, 2023.

Dai, L., Zhang, L., Chen, D., and Zhao, Y.: Assessment of carbonaceous aerosols in suburban Nanjing under air pollution control measures: Insights from long-term measurements, Environ. Res., 212, 113302, doi: https://doi.org/10.1016/j.envres.2022.113302, 2022.

Dao, X., Ji, D., Zhang, X., He, J., Sun, J., Hu, J., Liu, Y., Wang, L., Xu, X., Tang, G., and Wang, Y.: Significant reduction in atmospheric organic and elemental carbon in PM2.5 in 2+26 cities in northern China, Environ. Res., 211, 113055, doi: https://doi.org/10.1016/j.envres.2022.113055, 2022.

Ding, J., Huang, W., Zhao, J., Li, L., Xiong, G., Jiang, C., Ye, D., Li, D., Wang, J., Yu, J., and Liu, R.: Characteristics and source origins of carbonaceous aerosol in fine particulate matter in a megacity, Sichuan Basin, southwestern China, Atmos. Pollut. Res., 13, 101266, doi: https://doi.org/10.1016/j.apr.2021.101266, 2022.

Liu, X., Zheng, M., Liu, Y., Jin, Y., Liu, J., Zhang, B., Yang, X., Wu, Y., Zhang, T., Xiang, Y., Liu, B., and Yan, C.: Intercomparison of equivalent black carbon (eBC) and elemental carbon (EC) concentrations with three-year continuous measurement in Beijing, China, Environ. Res., 209, 112791, doi: https://doi.org/10.1016/j.envres.2022.112791, 2022.

Liang, Y., Wu, C., Wu, D., Liu, B., Li, Y. J., Sun, J., Yang, H., Mao, X., Tan, J., Xia, R., Deng, T., Li, M., and Zhou, Z.: Vertical distributions of atmospheric black carbon in dry and wet seasons observed at a 356-m meteorological tower in Shenzhen, South China, Sci. Total. Environ., 853, 158657, doi: https://doi.org/10.1016/j.scitotenv.2022.158657, 2022.

He, G., Deng, T., Wu, D., Wu, C., Huang, X., Li, Z., Yin, C., Zou, Y., Song, L., Ouyang, S., Tao, L., and Zhang, X.: Characteristics of boundary layer ozone and its effect on surface ozone concentration in Shenzhen, China: A case study, Sci. Total. Environ., 791, 148044, doi: https://doi.org/10.1016/j.scitotenv.2021.148044, 2021.

Liang, Y., Wu, C., Jiang, S., Li, Y. J., Wu, D., Li, M., Cheng, P., Yang, W., Cheng, C., Li, L., Deng, T., Sun, J. Y., He, G., Liu, B., Yao, T., Wu, M., and Zhou, Z.: Field comparison of electrochemical gas sensor data correction algorithms for ambient air measurements, Sens. Actuators B Chem., 327, 128897, doi: https://doi.org/10.1016/j.snb.2020.128897, 2021.

Wu, C., Liu, B., Wu, D., Yang, H., Mao, X., Tan, J., Liang, Y., Sun, J. Y., Xia, R., Sun, J., He, G., Li, M., Deng, T., Zhou, Z., and Li, Y. J.: Vertical profiling of black carbon and ozone using a multicopter unmanned aerial vehicle (UAV) in urban Shenzhen of South China, Sci. Total. Environ., 801, 149689, doi: https://doi.org/10.1016/j.scitotenv.2021.149689, 2021.

Liu, B., Wu, C., Ma, N., Chen, Q., Li, Y., Ye, J., Martin, S. T., and Li, Y. J.: Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China, Sci. Total. Environ., 709, 136109, doi: https://doi.org/10.1016/j.scitotenv.2019.136109, 2020.

Wong, Y. K., Huang, X. H. H., Louie, P. K. K., Yu, A. L. C., Chan, D. H. L., and Yu, J. Z.: Tracking separate contributions of diesel and gasoline vehicles to roadside PM2.5 through online monitoring of volatile organic compounds and PM2.5 organic and elemental carbon: a 6-year study in Hong Kong, Atmos. Chem. Phys., 20, 9871-9882, doi: https://doi.org/10.5194/acp-20-9871-2020, 2020.

Sun, J. Y., Wu, C., Wu, D., Cheng, C., Li, M., Li, L., Deng, T., Yu, J. Z., Li, Y. J., Zhou, Q., Liang, Y., Sun, T., Song, L., Cheng, P., Yang, W., Pei, C., Chen, Y., Cen, Y., Nian, H., and Zhou, Z.: Amplification of black carbon light absorption induced by atmospheric aging: temporal variation at seasonal and diel scales in urban Guangzhou, Atmos. Chem. Phys., 20, 2445-2470, doi: https://doi.org/10.5194/acp-20-2445-2020, 2020a.

Sun, T., Wu, C., Wu, D., Liu, B., Sun, J. Y., Mao, X., Yang, H., Deng, T., Song, L., Li, M., Li, Y. J., and Zhou, Z.: Time-resolved black carbon aerosol vertical distribution measurements using a 356-m meteorological tower in Shenzhen, Theor. Appl. Climatol., 140, 1263–1276, doi: https://doi.org/10.1007/s00704-020-03168-6, 2020b.

Ji, D., Gao, W., Maenhaut, W., He, J., Wang, Z., Li, J., Du, W., Wang, L., Sun, Y., Xin, J., Hu, B., and Wang, Y.: Impact of air pollution control measures and regional transport on carbonaceous aerosols in fine particulate matter in urban Beijing, China: insights gained from long-term measurement, Atmos. Chem. Phys., 19, 8569-8590, doi: https://doi.org/10.5194/acp-19-8569-2019, 2019.

Wu, C., Wu, D., and Yu, J. Z.: Estimation and Uncertainty Analysis of Secondary Organic Carbon Using One-Year of Hourly Organic and Elemental Carbon Data, J. Geophys. Res., 124, 2774-2795, doi: https://doi.org/10.1029/2018JD029290, 2019.

Liu, B., He, M. M., Wu, C., Li, J., Li, Y., Lau, N. T., Yu, J. Z., Lau, A. K. H., Fung, J. C. H., Hoi, K. I., Mok, K. M., Chan, C. K., and Li, Y. J.: Potential exposure to fine particulate matter (PM2.5) and black carbon on jogging trails in Macau, Atmos. Environ., 198, 23-33, doi: https://doi.org/10.1016/j.atmosenv.2018.10.024, 2019.

Wang, N. and Yu, J. Z.: Size distributions of hydrophilic and hydrophobic fractions of water-soluble organic carbon in an urban atmosphere in Hong Kong, Atmos. Environ., 166, 110-119, doi: https://doi.org/10.1016/j.atmosenv.2017.07.009, 2017.

Wu, C., Huang, X. H. H., Ng, W. M., Griffith, S. M., and Yu, J. Z.: Inter-comparison of NIOSH and IMPROVE protocols for OC and EC determination: implications for inter-protocol data conversion, Atmos. Meas. Tech., 9, 4547-4560, doi: https://doi.org/10.5194/amt-9-4547-2016, 2016.

Zhou, Y., Huang, X. H. H., Griffith, S. M., Li, M., Li, L., Zhou, Z., Wu, C., Meng, J., Chan, C. K., Louie, P. K. K., and Yu, J. Z.: A field measurement based scaling approach for quantification of major ions, organic carbon, and elemental carbon using a single particle aerosol mass spectrometer, Atmos. Environ., 143, 300-312, doi: https://doi.org/10.1016/j.atmosenv.2016.08.054, 2016.

何国文, 吴兑, 吴晟, 李梅, 邓涛, 吴伟超, 李莉, 程鹏, 袁婧, 张颖龙, 宋烺, 孙嘉胤, 陶丽萍, 梁粤, 谭健, and 周振: 嘉兴夏季臭氧污染的近地层垂直变化特征, 中国环境科学, 40, 4265-4274, doi: https://doi.org/10.19674/j.cnki.issn1000-6923.2020.0474, 2020.

何国文, 邓涛, 欧阳珊珊, 陶丽萍, 李振宁, 吴晟, 张雪, and 吴兑: 广州地区秋季PM2.5和臭氧复合污染的观测研究, 环境科学学报, 42, 250-259, doi: https://doi.org/10.13671/j.hjkxxb.2021.0462, 2022.

夏瑞, 谭健, 汪琼琼, 吴兑, 孔少飞, 陈楠, 邓涛, 陶丽萍, 张雪, 吴柏禧, 吴良斌, 王庆, and 吴晟: 基于 PMF 的武汉市大气细颗粒物消光来源解析, 中国环境科学, 43, 7-19, doi: https://doi.org/10.19674/j.cnki.issn1000-6923.2023.0011, 2023.

孙嘉胤, 吴晟, 吴兑, 李梅, 邓涛, 杨闻达, 程鹏, 梁粤, 谭健, 何国文, 成春雷, 李磊, and 周振: 广州城区黑碳气溶胶吸光增强特性研究, 中国环境科学, 40, 4177-4189, doi: https://doi.org/10.19674/j.cnki.issn1000-6923.2020.0465, 2020.

孙天林, 吴兑, 吴晟, 孙嘉胤, 陈慧忠, 邓涛, 李梅, and 周振: 东莞市黑碳气溶胶污染特征及来源分析, 地球化学, 49, 287-297, doi: https://doi.org/10.19700/j.0379-1726.2019.05.011, 2020.

廖彤, 吴梦曦, 刘素琳, 廉秀峰, 蔡日东, 陈多宏, 成春雷, 杨帆, and 李梅: 臭氧对颗粒物中有机组分形成的关键作用, 环境科学学报, 43, 181-191, doi: https://doi.org/10.13671/j.hjkxxb.2022.0453, 2023.

谭健, 夏瑞, 吴兑, 孔少飞, 陈楠, 成春雷, 邓涛, 陶丽萍, 张雪, 吴柏禧, 吴良斌, 王庆, and 吴晟: 地壳元素铁的吸光贡献对黑碳吸光增强估算的影响——以武汉为例, 中国环境科学, 42, 3033-3045, doi: https://doi.org/10.19674/j.cnki.issn1000-6923.20220314.021, 2022.

Adoption in research publications (without acknowledgment):

Qiao, T., Zhao, M., Xiu, G., and Yu, J.: Seasonal variations of water soluble composition (WSOC, Hulis and WSIIs) in PM1 and its implications on haze pollution in urban Shanghai, China, Atmos. Environ., 123, Part B, 306-314, 2015. http://dx.doi.org/10.1016/j.atmosenv.2015.03.010

List of programs I developed:

ScatterPlot

Histogram and Boxplot

MRS

RT-ECOC raw data processor

Benchtop Sunset ECOC analyzer data processor

DRI 2001A data Sorter

SMPS Toolkit

Mie Scattering

Aethalometer data correction

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Scatter Plot是一个方便的工具,可以最大限度地提高大气科学中数据可视化的效率。 虽然有许多现有的通用数据可视化软件,但不能满足许多大气科学特定的研究目的,所以我开发自己的程序。 本程序包括WODR, Deming和York算法进行线性回归,这三种算法考虑了X和Y都包含不确定性(观测误差),对大气的应用而言更加客观地反映真实情况。它是基于Igor的,并且包含大量用于数据分析和图形绘图的有用功能,包括批量绘图,通过图形界面实现数据掩蔽,Z轴的颜色编码,根据数据或字符串进行过滤和分组。

有关Scatter Plot的评估和应用的更多细节,请参阅(如果你在文章中用到了本软件,请引用以下文章)

Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi: https://doi.org/10.5194/amt-11-1233-2018, 2018.

关于程序的最新信息可以在我的网站上找到:

https://www.x-mol.com/groups/wucheng

https://sites.google.com/site/wuchengust/

https://wucheng.weebly.com/

https://doi.org/10.5281/zenodo.832416